Abstract
Passive missiles including short-range or within visual range air to air missiles (SRAAMs or WVRAAMs) and Man portable air defense systems (MANPADS) are an extraordinary danger for regular civilian and military airplane. This postulation intends to find approaches towards that could be utilized in a passive missile approach warning system. The initial segment of the study depends on spectrum analysis of the missile plume and determination of most suitable range for identifying Passive missiles. By looking at the benefits, drawbacks of working in different ranges and furthermore dissecting their innovative difficulties, this thesis closed to choose solar blind UV (SBUV) range for rocket recognition to be most appropriate.
After this basic quest, the two main objectives of the missile detection system are to detect the missile after de-cluttering from background and classifying it as a threatening or approaching missile from a sequence of images. The detection is based on convolution neural networks (CNN), moving object tracking algorithms and post processors. The greatest challenge in detection through CNN models is the availability of training data. This requirement of data synthesis has been catered for in this research through 3d simulations in decided spectrum.
Direction of motion of the detected threats and speed of motion has been estimated using moving object tracking techniques and extended Kalman filter (EKF), and same has been utilized to classify the missiles as approaching or non-approaching missiles. Same information has also been used to classify a moving target as a missile or another flying jet. Position and range estimation of the threats can also be done using EKF but unfortunately some of the desired parameters for this calculation were not available without actual sensor. Therefore, same has been left for future research work.
app.blackhole.run
Passive missiles including short-range or within visual range air to air missiles (SRAAMs or WVRAAMs) and Man portable air defense systems (MANPADS) are an extraordinary danger for regular civilian and military airplane. This postulation intends to find approaches towards that could be utilized in a passive missile approach warning system. The initial segment of the study depends on spectrum analysis of the missile plume and determination of most suitable range for identifying Passive missiles. By looking at the benefits, drawbacks of working in different ranges and furthermore dissecting their innovative difficulties, this thesis closed to choose solar blind UV (SBUV) range for rocket recognition to be most appropriate.
After this basic quest, the two main objectives of the missile detection system are to detect the missile after de-cluttering from background and classifying it as a threatening or approaching missile from a sequence of images. The detection is based on convolution neural networks (CNN), moving object tracking algorithms and post processors. The greatest challenge in detection through CNN models is the availability of training data. This requirement of data synthesis has been catered for in this research through 3d simulations in decided spectrum.
Direction of motion of the detected threats and speed of motion has been estimated using moving object tracking techniques and extended Kalman filter (EKF), and same has been utilized to classify the missiles as approaching or non-approaching missiles. Same information has also been used to classify a moving target as a missile or another flying jet. Position and range estimation of the threats can also be done using EKF but unfortunately some of the desired parameters for this calculation were not available without actual sensor. Therefore, same has been left for future research work.
1 file shared with you
This file shared with BlackHole. Send any file using blockchain technology.
app.blackhole.run
